Why is Data Management so Important to Data Science?

High data availability may help power digital transformation, but data management systems are needed to keep that data organized and make it accessible. Read this article to see why data management is important to data science.




Data is at the core of all analytics tools and machine learning algorithms. It enables the leaders to get to the bottom of what moves the needle and cuts the mark with the customers. Put simply, data is an asset to any organization when used effectively and smartly. Gone are the days when organizations were data-deprived and did not have enough awareness to leverage its power. Recent times have shown that a lot of organizations have moved beyond the data constraints and have it in abundance to start the analytics drill. 

However, the data availability single-handedly does not resolve one-of-the-many issues organizations face in their digital transformation journey. They need data management systems in place that take birth from the marriage of IT and business teams.


Why is Data Management so Important to Data Science?
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So, let us first understand what is data management.  


Data Management


Data management, as the name suggests, is all things data - right from how the data is ingested, stored, organized, and maintained within an organization. Data management is conventionally owned by IT teams but effective data management is only possible through the cross-collaboration of IT teams with the business users in the loop. Business needs to provide the data requirements to the IT as they have better visibility of the end goal the organization is aiming to achieve.

Besides creating policies and best practices, the data management team is also tasked with a range of activities, as outlined here. Let us understand the scope of what all comes under data management:

  • Data Storage and updation - who will have access to edit the data and presumes data ownership
  • High Availability and Disaster Recovery
  • Data archival and retention policy to understand the data inventory and its utility
  • On-prem and multi-cloud data storage
  • Lastly and most importantly, data security and privacy to adhere to regulatory requirements.


Self-Serve Analytics - Accelerator of Business Value Generation 


Easy data access and self-serve analytics - the core pillars of data democratization, significantly increase the speed to generate actionable insights and business impact in turn. 

Let me elaborate on this a little more. Think of a case where a business analyst presents a report to the business leaders that focuses on solving a particular objective, say customer segmentation. Now, if the business needs to know some additional details that are not captured in the first draft of the analysis, they need to funnel down this request back to the analyst through the entire data cycle and wait for the updated results before they are in a position to take action.

As it must be evident by now, this leads to an uncalled delay in getting enough information on the table to empower all leaders and executives to trust the data and analysis and design the business strategy. Not only does such delay lead to lost business opportunity in terms of competitive edge, but the report along with the data also becomes stale by the time it is exhaustive as per the business needs. 

Great, so we have understood the problem now. Let us shift gears to how we can fill this gap between the business needs and the analysis presented. Now, one issue is clear in the scenario explained above - the current situation where the data is mostly handled and used by the analysts aka the tech users. Well-managed data systems enable non-tech business users (data consumers in general) to simply pull out the analysis of their needs and take timely decisions.


Data Management in Data Science


By now, we understand data management and its significance and the equation equally holds true in the light of data science projects and teams. 

Data sits at the heart of all machine learning algorithms. Data Science is the most ubiquitous consumer of organizational data. We need to give more emphasis on the words highlighted above - data science does not own the data, it is the consumer of the potentially (and wishfully!!!) well-managed and organized data.

Why potentially managed data - that's because more often than not, data is not present in its right form and shape. Echoing the voice and concerns of the data science community, data issues are what keep the data scientists on their toes most of the time. 

Data management teams and the entire organization in general needs to adopt the data-first culture and promote data literacy to ensure that the key strategic asset to the business i.e.data is looked after well and used well, too. 


When To Declare That An Organization Has Well-Managed Data Systems?  


Well, that's not an easy question to answer. One can not wait for the data management teams to give a green signal for the data science team to start consuming the data into their machine learning pipeline. A pragmatic way would be to lay strong groundwork for robust and effectively managed data teams, keeping in mind that it is an iterative process. Yes, just like the iterative nature of the machine learning algorithms, the underlying data management is also a lifecycle approach. It continues to evolve as data science works in collaboration with the data management teams in improving and enhancing the best practices and guidelines.

Having said that, the data management team is the sole owner of data-related policies, practices, and data-access protocols with strong data-governance frameworks.

With increased data creation over the pandemic era, a lot of organizations are aggressively looking to monetize the data in various ways including but not limited to understanding the end user better, improving operational efficiencies by understanding the internal processes, or by providing the better end-user experience. Hence, the focus on data and data governance frameworks has increased sharply over the last few years. 


Marrying Business, Data Management, And Data Science Teams


One word answer for this alignment to happen is effective data governance policies. All three teams need to have a strong channel for communication and feedback. Also, the teams’ receptiveness to iterate and improve the current data processes is the key accelerator of the organizations’ strong digital journey. 

In fact, data culture itself echoes that the data responsibilities are not just restricted to any particular team or an individual. It is the shared responsibility of each employee of the organization to contribute and establish the data processes of the highest standards.




This post was dedicated to all things data. It started from an understanding of the roles and responsibilities of data management teams in general. Towards the later half, the post focuses on the significance of data management with respect to the data science teams and how cross-team alignment can work wonders in establishing effective data processes in the organization.
Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.